Abstract—The traveling salesman problem (TSP) plays an
important role in theoretical computer science. It can be used
to solve different route planning problems in the real world
and has been proved to be NP-hard. Plenty of researchers tried
to solve the TSP by the population-based algorithms. However,
as a real-world problem, the TSP has two major differences
with the benchmark functions. One is the visiting order of the
TSP is a combination of integers and the other one is each city
in the problem has to be exactly visited once. To cross the two
problems, the standardized bare bones particle swarm
optimization (SBBPSO) algorithm is proposed in this work.
The Gaussian distribution is used to select the positions of
particles in the next generation. A standardized converter is
used to ensure the dimensions of each particle are integers and
each city has been visited exactly once. To test the performance
of the SBBPSO, several famous instances are used in the
experiments. Also, the standard bare bones particle swarm
optimizations algorithm is used as the control group. The
experimental results confirm that the SBBPSO is able to solve
Index Terms—Bare bones, particle swarm optimization, standardized, traveling salesman problem.
Jia Guo is with the Graduate School of Computer and Information Sciences, Hosei University, Tokyo, Japan (e-mail: email@example.com).
Yuji Sato is with the Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan (e-mail: firstname.lastname@example.org).
Cite: Jia Guo and Yuji Sato, "A Standardized Bare Bones Particle Swarm Optimization Algorithm for Traveling Salesman Problem," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 477-481, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).